Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of RNA01:23

Types of RNA

63.8K
Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...
63.8K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

917
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
917
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

9.2K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
9.2K
Regulated mRNA Transport02:22

Regulated mRNA Transport

6.3K
In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
6.3K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K
Bacterial RNA Polymerase00:43

Bacterial RNA Polymerase

29.6K
Unlike eukaryotes, bacteria use a single RNA Polymerase (RNAP) to transcribe all genes. The different subunits of bacterial RNAPhave distinct functions. The multisubunit structure of the bacterial RNAP helps the enzyme to maintain catalytic function, facilitate assembly, interact with DNA and RNA, and self-regulate its activity.
In most genes, the transcription site is a single base present upstream of the coding sequence. Though RNAP is a catalytically efficient enzyme, it does not recognize...
29.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Improving prediction of bacterial sRNA regulatory targets with expression data.

NAR genomics and bioinformatics·2025
Same author

Popcorn: prediction of short coding and noncoding genomic sequences in prokaryotes.

Bioinformatics (Oxford, England)·2025
Same author

Molecular mechanisms underlying the evolution of a color polyphenism by genetic accommodation in the tobacco hornworm, <i>Manduca sexta</i>.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

<i>Escherichia coli</i> transcriptome assembly from a compendium of RNA-seq data sets.

RNA biology·2023
Same journal

Defined bacterial consortium highlights the impact of intestinal bacteria on DNA methylation and tumorigenesis.

Genome biology·2026
Same journal

Somatic mobility of transposons is explosive and shaped by distinct integration biases in Arabidopsis thaliana.

Genome biology·2026
Same journal

UK Biobank whole-genome sequencing reveals robust contributions of rare variants to complex-trait heritability.

Genome biology·2026
Same journal

A one-week automated genome-wide optical pooled screen using OttoSeq.

Genome biology·2026
Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026
Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
12:49

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

10.1K

TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning.

Brian Tjaden1

  • 1Department of Computer Science, Wellesley College, Wellesley, MA, USA. btjaden@wellesley.edu.

Genome Biology
|December 2, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed TargetRNA3, a machine learning tool to predict targets of small regulatory RNAs (non-coding genes) in prokaryotes. This new method improves upon existing approaches for identifying gene interactions.

Keywords:
ProkaryotesRegulationTarget predictionsRNA

More Related Videos

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

6.7K
Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
11:00

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

Published on: June 12, 2018

13.9K

Related Experiment Videos

Last Updated: Jul 9, 2025

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
12:49

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

10.1K
Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

6.7K
Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
11:00

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

Published on: June 12, 2018

13.9K

Area of Science:

  • Microbiology
  • Molecular Biology
  • Bioinformatics

Background:

  • Small regulatory RNAs are crucial non-coding genes in prokaryotes, controlling gene expression through base-pairing interactions with messenger targets.
  • Identifying the targets of these small RNAs is essential for understanding gene regulation, but current methods face challenges.
  • The discovery of novel small RNAs necessitates advanced computational tools for efficient target prediction.

Purpose of the Study:

  • To develop and validate a novel computational method for predicting the targets of small regulatory RNAs in prokaryotes.
  • To improve the accuracy and efficiency of identifying small RNA-target interactions compared to existing approaches.
  • To provide a publicly accessible tool for researchers studying prokaryotic gene regulation.

Main Methods:

  • Utilized a machine learning approach to analyze thousands of known small RNA-target interactions.
  • Interrogated over one hundred distinct features indicative of RNA-RNA interactions, including sequence complementarity and structural properties.
  • Developed and trained the TargetRNA3 algorithm on a comprehensive dataset of validated interactions.

Main Results:

  • The TargetRNA3 method demonstrated superior performance in predicting small RNA targets compared to existing computational tools.
  • The machine learning model effectively integrated diverse features to accurately identify regulatory interactions.
  • Validation studies confirmed the high accuracy of TargetRNA3 in predicting functional targets.

Conclusions:

  • TargetRNA3 represents a significant advancement in the prediction of small regulatory RNA targets in prokaryotes.
  • The developed tool enhances our ability to elucidate gene regulatory networks mediated by small RNAs.
  • TargetRNA3 is available to the research community, facilitating further discoveries in prokaryotic molecular biology.